| | --- |
| | license: mit |
| | tags: |
| | - diffusion |
| | - llm |
| | - conversational |
| | - difference-labs |
| | datasets: |
| | - smangrul/ultrachat-10k-chatml |
| | base_model: |
| | - darwinkernelpanic/DiffReaper-5L |
| | --- |
| | |
| | # DiffReaper-6 |
| |
|
| | **DiffReaper-6** is a Large-scale Diffusion-based Large Language Model (Diffusion-LLM) developed by **DifferenceLabs**. |
| |
|
| | It represents a significant architectural leap over the previous 5L version, transitioning to a more robust denoiser and a deeper transformer-based backbone to achieve actual conversational coherence. |
| |
|
| | ## Model Details |
| | - **Architecture**: Diffusion-Transformer (DiT) with Adaptive Layer Norm (adaLN-Single) modulation. |
| | - **Backbone**: 24 Layers, 24 Attention Heads, 1536 Hidden Dimension. |
| | - **Tokenizer**: BERT-base-uncased. |
| | - **Training Objective**: MSE on Denoising Latents (Predicting original embeddings from noisy input). |
| | - **Conditioning**: Prompt-concatenated latents with time-step embedding. |
| |
|
| | ## Training |
| | The model is being trained on an RTX 5090 using the `ultrachat-10k` dataset, focusing on conversational flow and instruction following. |
| |
|
| | ## Goal |
| | To prove that diffusion models can reach (and eventually exceed) the coherence of auto-regressive models while maintaining the creative "soul" and parallel generation benefits of diffusion. |
